{"id":"W2954304876","doi":"10.5194/ica-adv-1-20-2019","title":"Modelling and Analysis of Semantically Enriched Simplified Trajectories Using Graph Databases","year":2019,"lang":"en","type":"article","venue":"Advances in Cartography and GIScience of the ICA","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary; University of New Brunswick","funders":"","keywords":"Computer science; Graph database; Trajectory; Geospatial analysis; Database; Visualization; Semantics (computer science); Graph; Data mining; Data structure; Process (computing); Theoretical computer science; Information retrieval; Algorithm; Programming language","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007934754,0.0007699112,0.0005701288,0.003613214,0.0005617935,0.002799559,0.001503264,0.0006918092,0.002082194],"category_scores_gemma":[0.003233124,0.0003494576,0.00163895,0.003877886,0.0006253926,0.002672285,0.001290169,0.0005990543,0.0006393528],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001450971,"about_ca_system_score_gemma":0.001508376,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03620161,"about_ca_topic_score_gemma":0.02738709,"domain_scores_codex":[0.9990336,0.0001902766,0.0001250323,0.0002436048,0.0003477504,0.0000598217],"domain_scores_gemma":[0.9986118,0.000425351,0.0001765428,0.0003453031,0.0003861586,0.00005479784],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002286858,0.0001174143,0.009758743,0.000452982,0.0001943387,0.001185116,0.0009016298,0.7890235,0.008836715,0.099763,0.006834591,0.08270316],"study_design_scores_gemma":[0.00001408311,0.00002843708,0.001323492,0.00003243939,0.00004100172,0.0001397536,0.0002555131,0.9592751,0.004288748,0.0180295,0.01654134,0.00003063542],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04958671,0.0002419785,0.9347237,0.0003128214,0.00006729108,0.0001806227,0.005713854,0.005639543,0.003533396],"genre_scores_gemma":[0.3945458,0.0009693274,0.5814584,0.00008330912,0.00003228245,0.0002545295,0.0191807,0.0006868429,0.0027888],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03620161,"threshold_uncertainty_score":0.07198179,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01952502297709935,"score_gpt":0.2694525390566923,"score_spread":0.2499275160795929,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}